Integrating Knowledge Management and Machine Learning for Water Treatment Optimization - A Case Study at Koot Amir Water Treatment Plant

Aug 5, 2026

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Optimal operation of water treatment plants faces persistent obstacles, including water-quality fluctuations, rising operational costs, and the need for rapid, intelligent decision-making. This applied study evaluates how machine learning algorithms can optimize performance at the Koot Amir Water Treatment Plant in Ahvaz, while highlighting knowledge management’s role. Using 40,000 records collected across five years, the workflow applies preprocessing, normalization, and model training/testing splits to predict water quality and guide chemical-usage optimization. Results show ANN achieves 94.7% accuracy, RF 92.1%, and SVM 89.3%, supporting improved effluent prediction and knowledge transfer.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

Methods

Results

Conclusions

Integrating Knowledge Management and Machine Learning for Water Treatment Optimization: A Case Study at Koot Amir Water Treatment Plant Hadi Alhaei1, Mansoor Koohi Rostami2􀀍, and Seyed Mohammad Ashrafi3 1. Department of Information Science and Knowledge, Shahid Chamran University of Ahvaz, Ahvaz, Iran. E-mail: [hadi_alhaei@yahoo.com](hadi_alhaei@yahoo.com) 2. Corresponding author, Department of Information Science and Knowledge, Shahid Chamran University ofAhvaz, Ahvaz, [Iran. E-mail:](Iran. E-mail: m.rostami@scu.ac.ir)[ m.rostami@scu.ac.ir](Iran. E-mail: m.rostami@scu.ac.ir) 3. . Department of Civil Engineering, Shahid Chamran University ofAhvaz, Ahvaz, Iran. E-mail: [ashrafi@scu.ac.ir](ashrafi@scu.ac.ir) | Article Info | | ABSTRACT | | --- | --- | --- | | Article type: | | Objective: In recent years, optimal operation of water treatment plants has faced | | Research Article | | numerous challenges, including fluctuations in water quality, rising operational costs, and the need for rapid and intelligent decision-making. In this context, the use of advanced knowledge management technologies, data mining, and artificial intelligence has | | Article history: | | emerged as powerful tools for optimizing operational processes. This study aims to | | Received 28 October 2024 | | evaluate the impact of machine learning algorithms on optimizing the performance of the | | Received in revised form 13 | | Koot Amir water treatment plant in Ahvaz, with an emphasis on the role of knowledge | | December 2024 | | management. | | Accepted 25 December 2024 | | Method: This applied research adopts a data-driven approach. The study population | | Available online 30 December | | comprises 40,000 records of real operational and qualitative data collected over five years | | 2024 | | from the Koot Amir treatment plant. After collection, the data underwent preprocessing and normalization and were divided into training (70%) and testing (30%) datasets. Three machine learning models—Artificial Neural Network (ANN), Random Forest (RF), and | | Keywords: | | Support Vector Machine (SVM)—were evaluated for predicting water quality and | | knowledge management, | | optimizing chemical usage. Data analysis and modeling were performed using Python, | | conceptual model, | | SPSS, and Excel. | | machine learning, | | Results: The evaluation results revealed that the Artificial Neural Network model | | koot amir water treatment plant, | | achieved the highest performance, with 94.7% accuracy and a determination index of | | performance optimization | | 0.91 in predicting water quality changes. The Random Forest model also demonstrated strong capabilities, with 92.1% accuracy and a determination index of 0.88, effectively identifying complex water quality patterns. The Support Vector Machine model showed lower performance, with 89.3% accuracy and higher error rates. Implementing knowledge management using these models facilitated improved prediction of effluent water quality and enhanced the transfer of operational knowledge to plant operators. Conclusions: This study demonstrates that integrating knowledge management with machine learning is an effective strategy for optimizing the performance of water treatment plants and can serve as a model for similar facilities. The adoption of advanced technologies holds significant potential for improving predictive capabilities and knowledge transfer in data-driven organizations. | | Cite this article: Alhaei, H., Koohi Rostami, M., & Ashrafi, M. (2024) . Integrating knowledge management and machine learning for water treatment optimization: A case study at Koot Amir Water Treatment Plant. Academic Librarianship and Information Research, 58 (4), 1-22. [http//doi.org/ 10.22059/jlib.2025.391697.1774](http//doi.org/ 10.22059/jlib.2025.391697.1774) | | | | | © The Author(s) . Publisher: University of Tehran.
DOI: [http//doi.org/ 10.22059/jlib.2025.391697.1774](http//doi.org/ 10.22059

FAQs

What problem does the study address in water treatment plant operations?

It targets challenges such as fluctuating water quality, increasing operational costs, and the need for faster, more intelligent decision-making during plant operations.

How was the machine learning evaluation conducted?

The research used 40,000 real operational and qualitative records from five years, applied preprocessing and normalization, split data into 70% training and 30% testing, and evaluated ANN, Random Forest, and SVM models.

Which model performed best for predicting water quality and how did knowledge management contribute?

The ANN model achieved the highest performance (94.7% accuracy). Knowledge management integrated with these models improved effluent water-quality prediction and supported transferring operational knowledge to plant operators.

Aug 5, 2026

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